Embracing causality in fault reasoning
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Neural Computation
Varieties of Helmholtz machine
Neural Networks - 1996 Special issue: four major hypotheses in neuroscience
Learning in graphical models
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Graphical Models in Applied Multivariate Statistics
Graphical Models in Applied Multivariate Statistics
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Graphical models form a successful probabilistic modeling approach: They encode relationships among a set of random variables and provide a representation for the joint probability distribution over these variables. The advantages of the graphical formalism are its origins in probability theory and graph theory, the structural modularity favoring parallel computations, and its visual appeal. In this paper, we discuss a method for constructing a particular instance of graphical models (the Helmholtz machine) by using an evolutionary approach. Particularly, we focus on the explaining away phenomenon difficult to address but potentially improving a graphical model qualitatively. Additionally, we provide initial simulation results for a case study.